Spatial Predictive Analysis: Chicago Graffiti Removal 311 Requests

MUSA 5080 Lab Assignment 4

Introduction

Graffiti represents a persistent challenge to urban neighborhoods, affecting aesthetic quality, public safety perceptions, and property values. Chicago’s 311 service request system provides a comprehensive record of graffiti removal requests across the city. This analysis applies spatial predictive modeling to understand whether we can forecast graffiti concentrations using spatial features and count regression models.

Analysis Goals: - Develop a spatial predictive model using k-nearest neighbor and Local Moran’s I features - Compare Poisson and Negative Binomial regression performance - Validate models using spatial cross-validation (Leave-One-Group-Out) - Test temporal stability using 2018 crime data - Evaluate predictive accuracy against kernel density estimation baseline

---

Part 1: Data Loading and Exploration

Loading Required Libraries

library(tidyverse)
library(lubridate)
library(sf)
library(sp)
library(raster)
library(spdep)
library(spatstat)
library(ggplot2)
library(gridExtra)
library(viridis)
library(MASS)
library(broom)
library(jsonlite)
library(httr)

theme_set(theme_minimal())

Downloading Graffiti Data from Chicago 311 Portal

# Chicago 311 API endpoint for graffiti data
api_url <- "https://data.cityofchicago.org/resource/hec5-y4x5.json"

# Query parameters: download all available data
params <- list(
  "$limit" = 50000
)

# Execute API request
response <- GET(api_url, query = params)
graffiti_raw <- fromJSON(content(response, "text"))
graffiti_df <- as_tibble(graffiti_raw)

cat("Raw Graffiti Data Summary:\n")

Raw Graffiti Data Summary:

cat("Total Records:", nrow(graffiti_df), "\n")

Total Records: 50000 

cat("Variables:", ncol(graffiti_df), "\n\n")

Variables: 23 

# Check available columns
cat("Available columns:\n")

Available columns:

print(colnames(graffiti_df))

 [1] "creation_date"                           
 [2] "status"                                  
 [3] "completion_date"                         
 [4] "service_request_number"                  
 [5] "type_of_service_request"                 
 [6] "what_type_of_surface_is_the_graffiti_on_"
 [7] "where_is_the_graffiti_located_"          
 [8] "street_address"                          
 [9] "zip_code"                                
[10] "x_coordinate"                            
[11] "y_coordinate"                            
[12] "ward"                                    
[13] "police_district"                         
[14] "community_area"                          
[15] "latitude"                                
[16] "longitude"                               
[17] "location"                                
[18] ":@computed_region_awaf_s7ux"             
[19] ":@computed_region_6mkv_f3dw"             
[20] ":@computed_region_vrxf_vc4k"             
[21] ":@computed_region_bdys_3d7i"             
[22] ":@computed_region_43wa_7qmu"             
[23] "ssa"                                     

Data Cleaning and Preparation

# Clean and prepare graffiti data
graffiti_clean <- graffiti_df %>%
  filter(!is.na(longitude) & !is.na(latitude)) %>%
  mutate(
    longitude = as.numeric(longitude),
    latitude = as.numeric(latitude),
    creation_date = as.POSIXct(creation_date),
    year = year(creation_date),
    month = month(creation_date),
    date = as_date(creation_date)
  ) %>%
  filter(longitude > -88.5 & longitude < -87.5,
         latitude > 41.6 & latitude < 42.1)

cat("Data Cleaning Summary:\n")

Data Cleaning Summary:

cat("Records after cleaning: ", nrow(graffiti_clean), "\n")

Records after cleaning:  49973 

if(nrow(graffiti_clean) > 0) {
  cat("Date range: ", min(graffiti_clean$date, na.rm = TRUE), 
      " to ", max(graffiti_clean$date, na.rm = TRUE), "\n")
}

Date range:  17709  to  17883 

head(graffiti_clean, 5)

# A tibble: 5 × 26
  creation_date       status    completion_date         service_request_number
  <dttm>              <chr>     <chr>                   <chr>                 
1 2018-12-18 00:00:00 Completed 2018-12-19T00:00:00.000 18-03388768           
2 2018-12-18 00:00:00 Completed 2018-12-19T00:00:00.000 18-03388476           
3 2018-12-18 00:00:00 Open      <NA>                    18-03386436           
4 2018-12-18 00:00:00 Completed 2018-12-19T00:00:00.000 18-03386535           
5 2018-12-18 00:00:00 Open      <NA>                    18-03388464           
# ℹ 22 more variables: type_of_service_request <chr>,
#   what_type_of_surface_is_the_graffiti_on_ <chr>,
#   where_is_the_graffiti_located_ <chr>, street_address <chr>, zip_code <chr>,
#   x_coordinate <chr>, y_coordinate <chr>, ward <chr>, police_district <chr>,
#   community_area <chr>, latitude <dbl>, longitude <dbl>, location <df[,2]>,
#   `:@computed_region_awaf_s7ux` <chr>, `:@computed_region_6mkv_f3dw` <chr>,
#   `:@computed_region_vrxf_vc4k` <chr>, `:@computed_region_bdys_3d7i` <chr>, …

Converting to Spatial Object

# Convert to sf object (WGS84)
graffiti_wgs84 <- st_as_sf(graffiti_clean,
                            coords = c("longitude", "latitude"),
                            crs = 4326)

# Project to UTM Zone 16N for accurate distance calculations
graffiti_utm <- st_transform(graffiti_wgs84, crs = 32616)

cat("Spatial Object Created:\n")

Spatial Object Created:

cat("Total features:", nrow(graffiti_utm), "\n")

Total features: 49973 

Loading Chicago City Boundary

# Load Chicago boundary from city's open data portal
chicago_url <- "https://data.cityofchicago.org/api/geospatial/igwz-8jzy?method=export&format=GeoJSON"
chicago_boundary <- st_read(chicago_url, quiet = TRUE)

# Project to UTM Zone 16N
chicago_utm <- st_transform(chicago_boundary, crs = 32616)

cat("Chicago Boundary Loaded\n")

Chicago Boundary Loaded

cat("Area:", round(as.numeric(st_area(chicago_utm))/1e6, 1), "km²\n")

Area: 4.8 9.1 6 6.6 5.3 8.1 8.2 7.1 2.9 11.3 6 8.3 6.5 5 10.2 8.3 9.6 2.6 10.1 3 5.1 9.3 9.3 11.8 18.5 3.4 5 14.7 8.3 11.9 7.6 4.3 4.6 2.6 4.3 1.6 1.8 4.5 2.7 3.9 4.2 5.4 7.6 7.6 3.2 8.7 1.6 4.5 12.5 5.5 28.2 7.7 9.2 9.1 13.5 10.9 5.2 7 3.7 5.4 12.5 3 5.7 6.6 7.6 9.1 8.2 8 9.2 12.6 9.8 8.2 7.4 7 8.5 34.5 4.5 km²

Visualizing Spatial Distribution

# Create base map showing all graffiti points
ggplot() +
  geom_sf(data = chicago_utm, fill = NA, color = "black", linewidth = 0.8) +
  geom_sf(data = graffiti_utm, color = "#E31C23", size = 0.8, alpha = 0.3) +
  theme_minimal() +
  labs(
    title = "Spatial Distribution of Graffiti Removal Requests",
    subtitle = paste("Total requests:", nrow(graffiti_utm)),
    x = "UTM Easting (m)",
    y = "UTM Northing (m)"
  ) +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    plot.subtitle = element_text(size = 11),
    axis.text = element_text(size = 9)
  )

Temporal Patterns

# Calculate monthly statistics
monthly_stats <- graffiti_clean %>%
  group_by(month) %>%
  summarise(
    count = n(),
    avg_per_day = n() / n_distinct(date),
    .groups = "drop"
  ) %>%
  mutate(month_name = month.abb[month])

# Visualize monthly distribution
ggplot(monthly_stats, aes(x = reorder(month_name, month), y = count)) +
  geom_col(fill = "#E31C23", alpha = 0.8, color = "black", linewidth = 0.3) +
  geom_text(aes(label = count), vjust = -0.3, size = 3.5, fontface = "bold") +
  theme_minimal() +
  labs(
    title = "Monthly Distribution of Graffiti Requests",
    x = "Month",
    y = "Number of Requests"
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(size = 13, face = "bold")
  )

cat("\nTemporal Summary:\n")

Temporal Summary:

print(monthly_stats)

# A tibble: 7 × 4
  month count avg_per_day month_name
  <dbl> <int>       <dbl> <chr>     
1     6  1337        334. Jun       
2     7 10078        325. Jul       
3     8 10098        326. Aug       
4     9  8033        268. Sep       
5    10  8808        284. Oct       
6    11  7733        258. Nov       
7    12  3886        216. Dec       

---

Part 2: Fishnet Grid Creation

Creating 500m × 500m Grid

# Create regular grid covering Chicago
fishnet_full <- st_make_grid(
  chicago_utm,
  cellsize = 500,
  square = TRUE,
  what = "polygons"
) %>%
  st_as_sf() %>%
  mutate(grid_id = row_number())

# Keep only grid cells intersecting Chicago boundary
fishnet_chicago <- fishnet_full[chicago_utm, ]

cat("Fishnet Grid Created:\n")

Fishnet Grid Created:

cat("Total grid cells:", nrow(fishnet_chicago), "\n")

Total grid cells: 2618 

cat("Cell size: 500m × 500m\n")

Cell size: 500m × 500m

Aggregating Graffiti Counts to Grid

# Aggregate graffiti points to grid cells
graffiti_grid <- fishnet_chicago %>%
  st_join(graffiti_utm, join = st_contains) %>%
  group_by(grid_id) %>%
  summarise(
    n_graffiti = n(),
    .groups = "drop"
  ) %>%
  mutate(n_graffiti = ifelse(is.na(n_graffiti), 0, n_graffiti))

cat("Grid Aggregation Complete:\n")

Grid Aggregation Complete:

cat("Total cells:", nrow(graffiti_grid), "\n")

Total cells: 2618 

cat("Cells with graffiti:", sum(graffiti_grid$n_graffiti > 0), "\n")

Cells with graffiti: 2618 

cat("Mean per cell:", round(mean(graffiti_grid$n_graffiti), 2), "\n\n")

Mean per cell: 19.47 

print(summary(graffiti_grid$n_graffiti))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    1.00    3.00   19.47   18.00  403.00 

Visualizing Grid Aggregation

# Map showing graffiti counts per grid cell
p1 <- ggplot() +
  geom_sf(data = graffiti_grid, aes(fill = n_graffiti), color = NA) +
  geom_sf(data = chicago_utm, fill = NA, color = "black", linewidth = 0.5) +
  scale_fill_viridis_c(name = "Requests", option = "plasma") +
  theme_minimal() +
  labs(
    title = "Graffiti Count Aggregation to 500m Grid",
    x = "UTM Easting (m)",
    y = "UTM Northing (m)"
  ) +
  theme(plot.title = element_text(size = 13, face = "bold"))

# Histogram of counts
p2 <- ggplot(graffiti_grid, aes(x = n_graffiti)) +
  geom_histogram(bins = 40, fill = "#E31C23", alpha = 0.8, color = "black") +
  theme_minimal() +
  labs(
    title = "Distribution of Graffiti Counts",
    x = "Requests per Cell",
    y = "Frequency"
  ) +
  theme(plot.title = element_text(size = 12, face = "bold"))

gridExtra::grid.arrange(p1, p2, ncol = 2)

---

Part 3: Spatial Features Construction

Creating Spatial Weights Matrix and Features

# Step 1: Ensure graffiti_grid is sf with grid_id
graffiti_grid <- graffiti_grid %>%
  mutate(grid_id = row_number())

# Step 2: Convert to sp object (required by spdep)
graffiti_sp <- as_Spatial(graffiti_grid)

# Step 3: Create k-nearest neighbors weight matrix
knn_neighbors <- knearneigh(coordinates(graffiti_sp), k = 5)
knn_weights <- knn2nb(knn_neighbors, sym = TRUE)
knn_weights_std <- nb2listw(knn_weights, style = "W")

cat("Spatial Weights Matrix Created (k=5 nearest neighbors)\n")

Spatial Weights Matrix Created (k=5 nearest neighbors)

# Step 4: Calculate k-NN feature (neighbor mean)
neighbor_lags <- lag.listw(knn_weights_std, graffiti_grid$n_graffiti)

# Step 5: Calculate Local Moran's I
local_moran <- localmoran(graffiti_grid$n_graffiti, knn_weights_std)

# Step 6: Calculate distance to hotspot
hotspot_threshold <- quantile(graffiti_grid$n_graffiti, 0.75)
hotspots <- graffiti_grid %>% filter(n_graffiti >= hotspot_threshold)
hotspot_center <- st_centroid(st_union(hotspots))
dist_to_hotspot <- as.numeric(st_distance(graffiti_grid, hotspot_center)) / 1000

# Step 7: Add all features to graffiti_grid
graffiti_grid <- graffiti_grid %>%
  mutate(
    neighbor_mean = neighbor_lags,
    local_i = local_moran[, 1],
    local_i_pval = local_moran[, 5],
    dist_to_hotspot = dist_to_hotspot,
    moran_cluster = case_when(
      local_i_pval >= 0.05 ~ "Not significant",
      local_i >= 0 & n_graffiti >= median(n_graffiti) ~ "High-High",
      local_i >= 0 & n_graffiti < median(n_graffiti) ~ "Low-Low",
      local_i < 0 & n_graffiti >= median(n_graffiti) ~ "High-Low",
      TRUE ~ "Low-High"
    )
  )

cat("\nSpatial Features Added:\n")

Spatial Features Added:

cat("neighbor_mean: Average graffiti count in 5 nearest neighbors\n")

neighbor_mean: Average graffiti count in 5 nearest neighbors

cat("local_i: Local Moran's I statistic\n")

local_i: Local Moran's I statistic

cat("dist_to_hotspot: Distance to graffiti hotspot (km)\n")

dist_to_hotspot: Distance to graffiti hotspot (km)

cat("moran_cluster: Classification of spatial clusters\n")

moran_cluster: Classification of spatial clusters

cat("\nFirst 5 rows:\n")

First 5 rows:

feature_summary <- graffiti_grid %>% 
  st_drop_geometry()

head(feature_summary, 5)

# A tibble: 5 × 7
  grid_id n_graffiti neighbor_mean local_i local_i_pval dist_to_hotspot
    <int>      <int>         <dbl>   <dbl>        <dbl>           <dbl>
1       1          1             1   0.200        0.317            27.9
2       2          1             1   0.200        0.317            28.0
3       3          1             1   0.200        0.273            28.1
4       4          1             1   0.200        0.317            28.2
5       5          1             1   0.200        0.317            28.3
# ℹ 1 more variable: moran_cluster <chr>

Visualizing Hotspots and Coldspots

ggplot() +
  geom_sf(data = graffiti_grid, aes(fill = moran_cluster), color = NA) +
  geom_sf(data = chicago_utm, fill = NA, color = "black", linewidth = 0.5) +
  scale_fill_manual(
    name = "Cluster Type",
    values = c(
      "High-High" = "#d73027",
      "Low-Low" = "#91bfdb",
      "High-Low" = "#fee090",
      "Low-High" = "#a6d96a",
      "Not significant" = "#f7f7f7"
    )
  ) +
  theme_minimal() +
  labs(
    title = "Local Moran's I: Graffiti Hotspots and Coldspots",
    x = "UTM Easting (m)",
    y = "UTM Northing (m)"
  ) +
  theme(plot.title = element_text(size = 13, face = "bold"))

cat("\nCluster Distribution:\n")

Cluster Distribution:

print(table(graffiti_grid$moran_cluster))

      High-High        High-Low        Low-High Not significant 
            304              20               2            2292 

---

Part 4: Count Regression Models

Preparing Data for Modeling

# Prepare data for modeling
model_data <- graffiti_grid %>%
  st_drop_geometry() %>%
  na.omit()

cat("Model Data Prepared:\n")

Model Data Prepared:

cat("Sample size:", nrow(model_data), "\n")

Sample size: 2618 

cat("Dependent variable (n_graffiti):\n")

Dependent variable (n_graffiti):

print(summary(model_data$n_graffiti))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    1.00    3.00   19.47   18.00  403.00 

cat("\nOverdispersion check:\n")

Overdispersion check:

cat("Mean:", mean(model_data$n_graffiti), "\n")

Mean: 19.46639 

cat("Variance:", var(model_data$n_graffiti), "\n")

Variance: 1706.421 

cat("Variance/Mean ratio:", round(var(model_data$n_graffiti)/mean(model_data$n_graffiti), 2), "\n")

Variance/Mean ratio: 87.66 

Poisson Regression

# Fit Poisson regression
poisson_model <- glm(
  n_graffiti ~ neighbor_mean + local_i + dist_to_hotspot,
  family = poisson(link = "log"),
  data = model_data
)

cat("POISSON REGRESSION RESULTS\n")

POISSON REGRESSION RESULTS

cat(strrep("=", 50), "\n\n")

================================================== 

print(summary(poisson_model))

Call:
glm(formula = n_graffiti ~ neighbor_mean + local_i + dist_to_hotspot, 
    family = poisson(link = "log"), data = model_data)

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      3.3068979  0.0126373  261.68   <2e-16 ***
neighbor_mean    0.0121075  0.0001187  101.97   <2e-16 ***
local_i          0.0780056  0.0009253   84.30   <2e-16 ***
dist_to_hotspot -0.0971570  0.0010339  -93.97   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 119805  on 2617  degrees of freedom
Residual deviance:  36856  on 2614  degrees of freedom
AIC: 45856

Number of Fisher Scoring iterations: 5

# Extract metrics
poisson_aic <- AIC(poisson_model)
poisson_deviance <- deviance(poisson_model)
poisson_dispersion <- poisson_deviance / df.residual(poisson_model)

cat("\nModel Fit Statistics:\n")

Model Fit Statistics:

cat("AIC:", round(poisson_aic, 2), "\n")

AIC: 45856.21 

cat("Dispersion statistic:", round(poisson_dispersion, 3), "\n")

Dispersion statistic: 14.099 

Negative Binomial Regression

# Fit Negative Binomial regression
nb_model <- glm.nb(
  n_graffiti ~ neighbor_mean + local_i + dist_to_hotspot,
  data = model_data
)

cat("\nNEGATIVE BINOMIAL REGRESSION RESULTS\n")

NEGATIVE BINOMIAL REGRESSION RESULTS

cat(strrep("=", 50), "\n\n")

================================================== 

print(summary(nb_model))

Call:
glm.nb(formula = n_graffiti ~ neighbor_mean + local_i + dist_to_hotspot, 
    data = model_data, init.theta = 1.254353791, link = log)

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      2.4862158  0.0557367  44.606   <2e-16 ***
neighbor_mean    0.0326243  0.0008968  36.378   <2e-16 ***
local_i         -0.0161810  0.0121606  -1.331    0.183    
dist_to_hotspot -0.0826818  0.0034258 -24.135   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Negative Binomial(1.2544) family taken to be 1)

    Null deviance: 8149.6  on 2617  degrees of freedom
Residual deviance: 2538.9  on 2614  degrees of freedom
AIC: 16295

Number of Fisher Scoring iterations: 1

              Theta:  1.2544 
          Std. Err.:  0.0385 

 2 x log-likelihood:  -16285.0510 

# Extract metrics
nb_aic <- AIC(nb_model)
nb_deviance <- deviance(nb_model)

cat("\nModel Fit Statistics:\n")

Model Fit Statistics:

cat("AIC:", round(nb_aic, 2), "\n")

AIC: 16295.05 

Model Comparison

# Compare models
cat("\nMODEL COMPARISON\n")

MODEL COMPARISON

cat(strrep("=", 50), "\n")

================================================== 

cat("Poisson AIC:", round(poisson_aic, 2), "\n")

Poisson AIC: 45856.21 

cat("Negative Binomial AIC:", round(nb_aic, 2), "\n")

Negative Binomial AIC: 16295.05 

cat("Difference:", round(poisson_aic - nb_aic, 2), "\n\n")

Difference: 29561.15 

if (nb_aic < poisson_aic) {
  cat("Decision: Negative Binomial model preferred (lower AIC)\n")
  selected_model <- nb_model
  model_name <- "Negative Binomial"
} else {
  cat("Decision: Poisson model preferred (lower AIC)\n")
  selected_model <- poisson_model
  model_name <- "Poisson"
}

Decision: Negative Binomial model preferred (lower AIC)

# Extract coefficients
coef_table <- tidy(selected_model, exponentiate = TRUE, conf.int = TRUE)

cat("\n\nSelected Model Coefficients (Exponentiated - Incidence Rate Ratios):\n")

Selected Model Coefficients (Exponentiated - Incidence Rate Ratios):

print(coef_table)

# A tibble: 4 × 7
  term            estimate std.error statistic   p.value conf.low conf.high
  <chr>              <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
1 (Intercept)       12.0    0.0557       44.6  0           10.6      13.6  
2 neighbor_mean      1.03   0.000897     36.4  9.44e-290    1.03      1.04 
3 local_i            0.984  0.0122       -1.33 1.83e-  1    0.960     1.01 
4 dist_to_hotspot    0.921  0.00343     -24.1  1.07e-128    0.914     0.927

---

Part 5: Spatial Cross-Validation

Setting Up Spatial Folds

# Create spatial folds
set.seed(123)
n_folds <- 5

centroids <- st_coordinates(st_centroid(graffiti_grid))
spatial_folds <- kmeans(centroids, centers = n_folds)$cluster

cv_data <- model_data %>%
  mutate(fold = spatial_folds[row_number()])

cat("Spatial Cross-Validation Setup:\n")

Spatial Cross-Validation Setup:

cat("Number of folds:", n_folds, "\n")

Number of folds: 5 

cat("Fold sizes:\n")

Fold sizes:

print(table(cv_data$fold))

  1   2   3   4   5 
497 331 505 610 675 

Running Cross-Validation

# Cross-validation loop
cv_results <- tibble()

for (fold_num in 1:n_folds) {
  train_data <- cv_data %>% filter(fold != fold_num)
  test_data <- cv_data %>% filter(fold == fold_num)
  
  # Fit model on training data
  if (model_name == "Negative Binomial") {
    fold_model <- glm.nb(
      n_graffiti ~ neighbor_mean + local_i + dist_to_hotspot,
      data = train_data
    )
  } else {
    fold_model <- glm(
      n_graffiti ~ neighbor_mean + local_i + dist_to_hotspot,
      family = poisson(link = "log"),
      data = train_data
    )
  }
  
  # Predictions
  pred <- predict(fold_model, newdata = test_data, type = "response")
  
  fold_results <- test_data %>%
    mutate(
      fold = fold_num,
      observed = n_graffiti,
      predicted = pred,
      error = observed - predicted,
      abs_error = abs(error),
      squared_error = error^2
    )
  
  cv_results <- bind_rows(cv_results, fold_results)
}

cat("Cross-Validation Complete:\n")

Cross-Validation Complete:

cat("Total predictions:", nrow(cv_results), "\n")

Total predictions: 2618 

Cross-Validation Error Metrics

# Calculate error metrics
mae <- mean(cv_results$abs_error, na.rm = TRUE)
rmse <- sqrt(mean(cv_results$squared_error, na.rm = TRUE))
me <- mean(cv_results$error, na.rm = TRUE)

cat("SPATIAL CROSS-VALIDATION RESULTS\n")

SPATIAL CROSS-VALIDATION RESULTS

cat(strrep("=", 50), "\n")

================================================== 

cat("Mean Absolute Error (MAE):", round(mae, 3), "\n")

Mean Absolute Error (MAE): 44.017 

cat("Root Mean Squared Error (RMSE):", round(rmse, 3), "\n")

Root Mean Squared Error (RMSE): 383.165 

cat("Mean Error (ME):", round(me, 3), "\n")

Mean Error (ME): -33.302 

Visualizing CV Results

# Observed vs Predicted
p1 <- ggplot(cv_results, aes(x = observed, y = predicted)) +
  geom_point(alpha = 0.5, color = "#E31C23") +
  geom_abline(intercept = 0, slope = 1, color = "blue", linetype = "dashed") +
  theme_minimal() +
  labs(
    title = "Spatial CV: Observed vs Predicted",
    x = "Observed",
    y = "Predicted",
    subtitle = paste("MAE =", round(mae, 2))
  ) +
  theme(plot.title = element_text(size = 12, face = "bold"))

# Residuals
p2 <- ggplot(cv_results, aes(x = error)) +
  geom_histogram(bins = 30, fill = "#E31C23", alpha = 0.7) +
  geom_vline(xintercept = 0, color = "blue", linetype = "dashed") +
  theme_minimal() +
  labs(
    title = "Residuals Distribution",
    x = "Prediction Error",
    y = "Frequency"
  ) +
  theme(plot.title = element_text(size = 12, face = "bold"))

gridExtra::grid.arrange(p1, p2, ncol = 2)

---

Part 6: Temporal Validation (2018 Data)

Loading 2018 Crime Data

# Download 2018 crimes data
crime_url <- "https://data.cityofchicago.org/resource/3i3m-jwuy.json"

# Simple query without complex filters
response_2018 <- GET(crime_url, query = list("$limit" = 50000))
crime_2018_raw <- fromJSON(content(response_2018, "text"))
crime_2018_df <- as_tibble(crime_2018_raw)

cat("2018 Crime Data Loaded:\n")

2018 Crime Data Loaded:

cat("Total records:", nrow(crime_2018_df), "\n")

Total records: 50000 

cat("Columns:", ncol(crime_2018_df), "\n")

Columns: 22 

Preparing 2018 Data

# Clean 2018 data
crime_2018_clean <- crime_2018_df %>%
  filter(!is.na(longitude) & !is.na(latitude)) %>%
  mutate(
    longitude = as.numeric(longitude),
    latitude = as.numeric(latitude)
  ) %>%
  filter(longitude > -88.5 & longitude < -87.5,
         latitude > 41.6 & latitude < 42.1)

# Convert to sf
crime_2018_wgs84 <- st_as_sf(crime_2018_clean,
                             coords = c("longitude", "latitude"),
                             crs = 4326)

crime_2018_utm <- st_transform(crime_2018_wgs84, crs = 32616)

cat("2018 Data Cleaned:\n")

2018 Data Cleaned:

cat("Records:", nrow(crime_2018_utm), "\n")

Records: 48166 

Aggregating 2018 to Same Grid

# Aggregate to grid
crime_2018_grid <- fishnet_chicago %>%
  st_join(crime_2018_utm, join = st_contains) %>%
  group_by(grid_id) %>%
  summarise(
    n_crime = n(),
    .groups = "drop"
  ) %>%
  mutate(n_crime = ifelse(is.na(n_crime), 0, n_crime))

cat("2018 Aggregation:\n")

2018 Aggregation:

cat("Total cells:", nrow(crime_2018_grid), "\n")

Total cells: 2618 

cat("Cells with crimes:", sum(crime_2018_grid$n_crime > 0), "\n")

Cells with crimes: 2618 

Predicting 2018 with 2017 Model

# Prepare features
crime_2018_features <- crime_2018_grid %>%
  st_drop_geometry() %>%
  mutate(
    observed_2018 = n_crime
  ) %>%
  left_join(
    model_data %>% mutate(grid_id = row_number()),
    by = "grid_id"
  ) %>%
  na.omit()

cat("2018 Features Prepared:\n")

2018 Features Prepared:

cat("Records:", nrow(crime_2018_features), "\n")

Records: 1186 

# Make predictions using 2017 model
pred_2018 <- predict(selected_model, 
                     newdata = crime_2018_features, 
                     type = "response")

temporal_results <- crime_2018_features %>%
  mutate(
    predicted_2018 = pred_2018,
    error_2018 = observed_2018 - predicted_2018,
    abs_error_2018 = abs(error_2018),
    squared_error_2018 = error_2018^2
  )

cat("Temporal Validation:\n")

Temporal Validation:

cat("Predictions made:", nrow(temporal_results), "\n")

Predictions made: 1186 

Temporal Validation Metrics

# Calculate 2018 metrics
mae_2018 <- mean(temporal_results$abs_error_2018)
rmse_2018 <- sqrt(mean(temporal_results$squared_error_2018))

cat("TEMPORAL VALIDATION (2018)\n")

TEMPORAL VALIDATION (2018)

cat(strrep("=", 50), "\n")

================================================== 

cat("MAE:", round(mae_2018, 3), "\n")

MAE: 41.618 

cat("RMSE:", round(rmse_2018, 3), "\n")

RMSE: 201.111 

---

Part 7: KDE Baseline Comparison

Calculating KDE Baseline

# Create point pattern
graffiti_coords <- st_coordinates(graffiti_utm)

# Create window
window <- owin(xrange = range(graffiti_coords[, 1]),
               yrange = range(graffiti_coords[, 2]))

# Create point pattern
graffiti_ppp <- ppp(x = graffiti_coords[, 1],
                    y = graffiti_coords[, 2],
                    window = window)

# Calculate KDE
kde_2017 <- density(graffiti_ppp, sigma = bw.diggle(graffiti_ppp))
kde_raster <- raster(kde_2017)

# Extract to grid
grid_centroids <- st_centroid(graffiti_grid)
kde_values <- raster::extract(kde_raster, as_Spatial(grid_centroids))

# Normalize
kde_grid_counts <- kde_values * (500 * 500) / sum(kde_values, na.rm = TRUE) * 
                   sum(graffiti_grid$n_graffiti)

# Calculate errors
kde_errors <- graffiti_grid %>%
  st_drop_geometry() %>%
  mutate(
    predicted_kde = kde_grid_counts,
    error_kde = n_graffiti - predicted_kde,
    abs_error_kde = abs(error_kde),
    squared_error_kde = error_kde^2
  ) %>%
  na.omit()

mae_kde <- mean(kde_errors$abs_error_kde)
rmse_kde <- sqrt(mean(kde_errors$squared_error_kde))

cat("KDE BASELINE\n")

KDE BASELINE

cat(strrep("=", 50), "\n")

================================================== 

cat("MAE:", round(mae_kde, 3), "\n")

MAE: 5204537 

cat("RMSE:", round(rmse_kde, 3), "\n")

RMSE: 13632995 

---

Part 8: Final Model Comparison

Comprehensive Comparison

# Compare all methods
comparison <- tibble(
  Method = c(
    paste("Spatial CV -", model_name),
    "Temporal (2018)",
    "KDE Baseline"
  ),
  MAE = c(mae, mae_2018, mae_kde),
  RMSE = c(rmse, rmse_2018, rmse_kde)
)

cat("FINAL MODEL COMPARISON\n")

FINAL MODEL COMPARISON

cat(strrep("=", 50), "\n")

================================================== 

print(comparison)

# A tibble: 3 × 3
  Method                               MAE      RMSE
  <chr>                              <dbl>     <dbl>
1 Spatial CV - Negative Binomial      44.0      383.
2 Temporal (2018)                     41.6      201.
3 KDE Baseline                   5204537.  13632995.

cat("\nImprovement Over KDE Baseline:\n")

Improvement Over KDE Baseline:

cat("Spatial CV:", round((1 - mae/mae_kde)*100, 1), "%\n")

Spatial CV: 100 %

cat("Temporal:", round((1 - mae_2018/mae_kde)*100, 1), "%\n")

Temporal: 100 %

# Visualize comparison
comparison_long <- comparison %>%
  pivot_longer(cols = c(MAE, RMSE), names_to = "Metric", values_to = "Value")

ggplot(comparison_long, aes(x = Method, y = Value, fill = Metric)) +
  geom_col(position = "dodge", alpha = 0.8, color = "black") +
  geom_text(aes(label = round(Value, 2)), position = position_dodge(width = 0.9), 
            vjust = -0.3, size = 3.5) +
  theme_minimal() +
  labs(
    title = "Final Model Comparison: Error Metrics",
    y = "Error (lower is better)"
  ) +
  scale_fill_brewer(palette = "Set2") +
  theme(
    plot.title = element_text(size = 13, face = "bold"),
    axis.text.x = element_text(angle = 30, hjust = 1)
  )

---

Part 9: Error Analysis and Performance Mapping

Mapping Prediction Errors

# Add predictions back to grid
graffiti_grid_errors <- graffiti_grid %>%
  left_join(
    cv_results %>%
      group_by(grid_id) %>%
      summarise(
        predicted = mean(predicted, na.rm = TRUE),
        error = mean(error, na.rm = TRUE),
        abs_error = mean(abs_error, na.rm = TRUE),
        .groups = "drop"
      ),
    by = "grid_id"
  )

# Map absolute errors
p1 <- ggplot() +
  geom_sf(data = graffiti_grid_errors, aes(fill = abs_error), color = NA) +
  geom_sf(data = chicago_utm, fill = NA, color = "black", linewidth = 0.5) +
  scale_fill_viridis_c(name = "Absolute Error", option = "magma", na.value = "white") +
  theme_minimal() +
  labs(
    title = "Geographic Distribution of Absolute Prediction Errors",
    x = "UTM Easting (m)",
    y = "UTM Northing (m)"
  ) +
  theme(plot.title = element_text(size = 12, face = "bold"))

# Map signed errors
p2 <- ggplot() +
  geom_sf(data = graffiti_grid_errors, aes(fill = error), color = NA) +
  geom_sf(data = chicago_utm, fill = NA, color = "black", linewidth = 0.5) +
  scale_fill_distiller(
    name = "Error",
    type = "div",
    palette = "RdBu",
    na.value = "white"
  ) +
  theme_minimal() +
  labs(
    title = "Signed Errors: Red=Underpredicted, Blue=Overpredicted",
    x = "UTM Easting (m)",
    y = "UTM Northing (m)"
  ) +
  theme(plot.title = element_text(size = 12, face = "bold"))

gridExtra::grid.arrange(p1, p2, ncol = 2)

Performance by Observed Count Level

# Analyze error patterns
error_by_count <- cv_results %>%
  mutate(count_bin = cut(observed, 
                        breaks = c(-Inf, 0, 5, 10, 20, Inf),
                        labels = c("0", "1-5", "6-10", "11-20", "20+"))) %>%
  group_by(count_bin) %>%
  summarise(
    n = n(),
    MAE = mean(abs_error, na.rm = TRUE),
    RMSE = sqrt(mean(squared_error, na.rm = TRUE)),
    .groups = "drop"
  )

cat("Error Metrics by Observed Count Level:\n\n")

Error Metrics by Observed Count Level:

print(error_by_count)

# A tibble: 4 × 4
  count_bin     n    MAE  RMSE
  <fct>     <int>  <dbl> <dbl>
1 1-5        1543   3.33  12.2
2 6-10        247  22.5  188. 
3 11-20       205  71.9  871. 
4 20+         623 144.   594. 

ggplot(error_by_count, aes(x = count_bin)) +
  geom_col(aes(y = MAE), fill = "#E31C23", alpha = 0.7) +
  geom_point(aes(y = RMSE), color = "#0073C2", size = 4) +
  theme_minimal() +
  labs(
    title = "Prediction Error by Observed Graffiti Count Level",
    x = "Observed Count Range",
    y = "Error Metric",
    subtitle = "Bars = MAE | Points = RMSE"
  ) +
  theme(plot.title = element_text(size = 12, face = "bold"))

---

Summary and Conclusions

Key Findings

cat("ANALYSIS SUMMARY\n")

ANALYSIS SUMMARY

cat(strrep("=", 50), "\n\n")

================================================== 

cat("1. DATA OVERVIEW\n")

1. DATA OVERVIEW

cat("   • Graffiti requests analyzed:", nrow(graffiti_utm), "\n")

   • Graffiti requests analyzed: 49973 

cat("   • Grid cells created:", nrow(graffiti_grid), "\n")

   • Grid cells created: 2618 

cat("   • Cells with graffiti:", sum(graffiti_grid$n_graffiti > 0), "\n\n")

   • Cells with graffiti: 2618 

cat("2. SPATIAL PATTERNS\n")

2. SPATIAL PATTERNS

cat("   • Significant clusters identified:", 
    sum(graffiti_grid$local_i_pval < 0.05), "\n")

   • Significant clusters identified: 326 

cat("   • Hotspot areas (High-High):", 
    sum(graffiti_grid$moran_cluster == "High-High"), "\n")

   • Hotspot areas (High-High): 304 

cat("   • Coldspot areas (Low-Low):", 
    sum(graffiti_grid$moran_cluster == "Low-Low"), "\n\n")

   • Coldspot areas (Low-Low): 0 

cat("3. MODEL PERFORMANCE\n")

3. MODEL PERFORMANCE

cat("   • Selected model:", model_name, "\n")

   • Selected model: Negative Binomial 

cat("   • Spatial CV MAE:", round(mae, 3), "\n")

   • Spatial CV MAE: 44.017 

cat("   • Spatial CV RMSE:", round(rmse, 3), "\n")

   • Spatial CV RMSE: 383.165 

cat("   • Improvement over KDE:", round((1 - mae/mae_kde)*100, 1), "%\n\n")

   • Improvement over KDE: 100 %

cat("4. TEMPORAL STABILITY\n")

4. TEMPORAL STABILITY

cat("   • 2018 predictions MAE:", round(mae_2018, 3), "\n")

   • 2018 predictions MAE: 41.618 

if (mae_2018 < mae * 1.3) {
  cat("   • Model shows good temporal stability\n")
} else {
  cat("   • Model shows temporal degradation\n")
}

   • Model shows good temporal stability

cat("\n5. SPATIAL FEATURES IMPORTANCE\n")

5. SPATIAL FEATURES IMPORTANCE

for (i in 2:nrow(coef_table)) {
  term <- coef_table$term[i]
  irr <- coef_table$estimate[i]
  pval <- coef_table$p.value[i]
  sig <- ifelse(pval < 0.05, "***", "")
  cat("   •", term, "(IRR =", round(irr, 3), ") ", sig, "\n")
}

   • neighbor_mean (IRR = 1.033 )  *** 
   • local_i (IRR = 0.984 )   
   • dist_to_hotspot (IRR = 0.921 )  *** 

---

Report Generated: 2025-11-16 01:37:30

Course: MUSA 5080 - Public Policy Analytics
Assignment: Lab 4 - Spatial Predictive Analysis
Institution: University of Pennsylvania